Contents

Contents 

#install.packages(c("patchwork", "tidylog", "leaflet", "htmltools", "highcharter"))

library(patchwork)
library(tidyverse)
library(tidylog)

── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──

✔ ggplot2 3.3.5     ✔ purrr   0.3.4
✔ tibble  3.1.6     ✔ dplyr   1.0.7
✔ tidyr   1.1.4     ✔ stringr 1.4.0
✔ readr   2.1.1     ✔ forcats 0.5.1

── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

Attaching package: ‘tidylog’

The following objects are masked from ‘package:dplyr’:

    add_count, add_tally, anti_join, count, distinct, distinct_all,
    distinct_at, distinct_if, filter, filter_all, filter_at, filter_if,
    full_join, group_by, group_by_all, group_by_at, group_by_if,
    inner_join, left_join, mutate, mutate_all, mutate_at, mutate_if,
    relocate, rename, rename_all, rename_at, rename_if, rename_with,
    right_join, sample_frac, sample_n, select, select_all, select_at,
    select_if, semi_join, slice, slice_head, slice_max, slice_min,
    slice_sample, slice_tail, summarise, summarise_all, summarise_at,
    summarise_if, summarize, summarize_all, summarize_at, summarize_if,
    tally, top_frac, top_n, transmute, transmute_all, transmute_at,
    transmute_if, ungroup

The following objects are masked from ‘package:tidyr’:

    drop_na, fill, gather, pivot_longer, pivot_wider, replace_na,
    spread, uncount

The following object is masked from ‘package:stats’:

    filter

options(repr.plot.width = 16, repr.plot.height = 8)

ggplot() +
  geom_bar(data = diamonds,
           aes(x = cut, fill = cut)
           ) +
  labs(x = "Cut of the Diamond", y = "Frequency") +
  theme(legend.position = "none") -> p1

ggplot() +
  geom_bar(data = diamonds,
           aes(x = color, fill = color)
           ) +
  labs(x = "Color of the Diamond", y = "Frequency") +
  theme(legend.position = "none") -> p2

ggplot() +
  geom_point(data = diamonds,
           aes(x = carat, y = price, color = cut)
           ) +
  labs(x = "Weight of the Diamond", y = "Price of the Diamond", color = "") +
  theme(legend.position = "bottom") -> p3

ggplot() +
  geom_boxplot(data = diamonds,
           aes(x = price, y = clarity, fill = cut)
           ) +
  labs(y = "Clarity of the Diamond", x = "Price of the Diamond", fill = "") +
  theme(legend.position = "bottom") -> p4

p1; p2; p3; p4
../../../_images/base_6_01.png ../../../_images/base_6_1.png ../../../_images/base_6_2.png ../../../_images/base_6_3.png 
p1 + p2 + p3
../../../_images/base_7_01.png 
(p1 + p2) / p3
../../../_images/base_8_01.png 
p1 | (p2 + p3)
../../../_images/base_9_01.png 
p1 | (p2 / p3)
../../../_images/base_10_01.png 
options(repr.plot.width = 16, repr.plot.height = 16)

(p1 + p2) / (p3 + p4)
../../../_images/base_11_01.png 
(p1 + p2) / (p3 + p4) +
  plot_annotation(
  title = 'The surprising truth about diamonds',
  subtitle = 'These plots will reveal untold secrets about one of our beloved data-sets',
  caption = 'Disclaimer: None of these plots are insightful',
  tag_levels = c('a', '1'),
  tag_prefix = 'Fig. ',
  tag_sep = '.',
  tag_suffix = ':'
  ) &
  theme(
    plot.tag.position = c(0, 1),
    plot.tag = element_text(size = 9, hjust = 0, vjust = 0, color = "steelblue")
    )
../../../_images/base_12_01.png 
options(repr.plot.width = 16, repr.plot.height = 8)

(p1 + plot_spacer() + p2 + plot_spacer() + p3)
../../../_images/base_13_01.png 
options(repr.plot.width = 16, repr.plot.height = 16)

p1 + p2 + p3 + p4 + 
  plot_layout(widths = c(2, 1))
../../../_images/base_14_01.png 
p1 + p2 + p3 + p4 + 
  plot_layout(
    widths = c(2, 1),
    heights = unit(c(5, 1), c('cm', 'null'))
    )
../../../_images/base_15_01.png 
layout <- "
##BBBB
AACCDD
##CCDD
"
p2 + p3 + p4 + p1 + 
  plot_layout(design = layout)
../../../_images/base_16_01.png 
layout <- c(
  area(t = 2, l = 1, b = 5, r = 4),
  area(t = 1, l = 3, b = 3, r = 5)
  )

p3 + p4 + 
  plot_layout(design = layout)
../../../_images/base_17_01.png 
layout <- '
A##
#B#
##C
'
wrap_plots(A = p1, B = p2, C = p3, design = layout)
../../../_images/base_18_01.png 
library(urbnmapr)

ggplot() +
  geom_polygon(
    data = states, 
    aes(x = long, y = lat, group = group, fill = state_abbv)
    ) +
  coord_fixed(1.3) +
  ggthemes::theme_map() + 
  theme(legend.position = "none") +
  labs(title = "Fixed!!") -> mymap

mymap + p1 + p2 + p3
../../../_images/base_19_01.png 
head(states)
A tibble: 6 × 9
longlatorderholepiecegroupstate_fipsstate_abbvstate_name
<dbl><dbl><int><lgl><fct><fct><chr><chr><chr>
-88.4732331.893861FALSE101.101ALAlabama
-88.4688831.930262FALSE101.101ALAlabama
-88.4686631.933173FALSE101.101ALAlabama
-88.4550432.039724FALSE101.101ALAlabama
-88.4549632.040585FALSE101.101ALAlabama
-88.4534232.053056FALSE101.101ALAlabama
 
head(counties)
A tibble: 6 × 12
longlatorderholepiecegroupcounty_fipsstate_abbvstate_fipscounty_namefips_classstate_name
<dbl><dbl><int><lgl><fct><fct><chr><chr><chr><chr><chr><chr>
-86.9176032.664171FALSE101001.101001AL01Autauga CountyH1Alabama
-86.8165732.660122FALSE101001.101001AL01Autauga CountyH1Alabama
-86.7133932.661733FALSE101001.101001AL01Autauga CountyH1Alabama
-86.7142232.705694FALSE101001.101001AL01Autauga CountyH1Alabama
-86.4131232.707395FALSE101001.101001AL01Autauga CountyH1Alabama
-86.4111732.409946FALSE101001.101001AL01Autauga CountyH1Alabama
 
ggplot() +
  geom_polygon(
      data = states, 
      aes(
          x = long, 
          y = lat, 
          group = group),
      fill = "white", 
      color = "steelblue"
  ) +
  coord_fixed(1.3)
../../../_images/base_22_01.png 
ggplot() +
  geom_polygon(
      data = states, 
      aes(
          x = long, 
          y = lat, 
          group = group, 
          fill = state_name),
      color = "white"
      ) +
  coord_fixed(1.3) +
  ggthemes::theme_map() +
  theme(legend.position = "none")
../../../_images/base_23_01.png 
head(statedata)
A tibble: 6 × 6
yearstate_fipsstate_namehhpophoratemedhhincome
<int><chr><chr><int><dbl><int>
201501Alabama 18463800.681432944700
201502Alaska 2501830.631186070600
201504Arizona 24630120.620617851000
201505Arkansas 11446570.654603142000
201506California128954710.537221964600
201508Colorado 20745170.638942563500
 
states %>%
  left_join(
      statedata, 
      by = c("state_fips", "state_name")
  ) -> state.df

left_join: added 4 columns (year, hhpop, horate, medhhincome)

           > rows only in x        0

           > rows only in y  (     0)

           > matched rows     83,933

           >                 ========

           > rows total       83,933

head(state.df)
A tibble: 6 × 13
longlatorderholepiecegroupstate_fipsstate_abbvstate_nameyearhhpophoratemedhhincome
<dbl><dbl><int><lgl><fct><fct><chr><chr><chr><int><int><dbl><int>
-88.4732331.893861FALSE101.101ALAlabama201518463800.681432944700
-88.4688831.930262FALSE101.101ALAlabama201518463800.681432944700
-88.4686631.933173FALSE101.101ALAlabama201518463800.681432944700
-88.4550432.039724FALSE101.101ALAlabama201518463800.681432944700
-88.4549632.040585FALSE101.101ALAlabama201518463800.681432944700
-88.4534232.053056FALSE101.101ALAlabama201518463800.681432944700
 
options(repr.plot.width = 24, repr.plot.height = 16)

ggplot() +
  geom_polygon(
      data = state.df, 
      aes(
          x = long, 
          y = lat, 
          group = group, 
          fill = medhhincome
          ),
          color = "white"
  ) +
  coord_fixed(1.3) +
  ggthemes::theme_map() +
  labs(
    title = "Median Household Income in the States (2015)",
    fill = "Median Household Income"
        ) +
  scale_fill_viridis_c(option = "magma") +
  theme(
      legend.position = "bottom",
      legend.text = element_text(size = 14),
      legend.key.width = unit(5, 'cm'),
      title = element_text(size = 20, face = "bold")
  )
../../../_images/base_27_01.png 
counties %>%
  left_join(
      countydata, 
      by = c("county_fips")
  ) -> county.df

left_join: added 4 columns (year, hhpop, horate, medhhincome)

           > rows only in x         0

           > rows only in y  (      0)

           > matched rows     208,874

           >                 =========

           > rows total       208,874

ggplot() +
  geom_polygon(data = county.df, 
               aes(x = long, y = lat, group = group, fill = medhhincome),
               color = "white", size = 0.05) +
  coord_fixed(1.3) +
  ggthemes::theme_map() +
  theme(
      legend.position = "bottom",
      legend.text = element_text(size = 14),
      legend.key.width = unit(5, 'cm'),
      title = element_text(size = 20, face = "bold")
  ) +
  labs(
    title = "Median Household Income in the Counties (2015)",
    fill = "Median Household Income"
        ) +
  scale_fill_viridis_c(option = "magma")
../../../_images/base_29_01.png 
county.df %>%
  filter(state_abbv == "FL") %>%
  ggplot() +
  geom_polygon(
               aes(x = long, y = lat, group = group,
                   fill = medhhincome),
               color = "white", size = 0.05
               ) +
  coord_fixed(1.3) +
  ggthemes::theme_map() +
  theme(
      legend.position = "bottom",
      legend.text = element_text(size = 14),
      legend.key.width = unit(5, 'cm'),
      title = element_text(size = 20, face = "bold")
  ) +
  labs(
    title = "Median Household Income in Floria Counties (2015)",
    fill = "Median Household Income"
        ) +
  scale_fill_viridis_c(option = "plasma")

filter: removed 203,791 rows (98%), 5,083 rows remaining
../../../_images/base_30_1.png 
library(tigris)
options(tigris_use_cache = TRUE)

places(
  state = "NH", cb = TRUE, year = 2018, progress_bar = FALSE
  ) -> places.nh

To enable 
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

places.nh %>%
    head()

Registered S3 method overwritten by 'geojsonsf':
  method        from   
  print.geojson geojson
A sf: 6 × 10
STATEFPPLACEFPPLACENSAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
<chr><chr><chr><chr><chr><chr><chr><dbl><dbl><POLYGON [°]>
13361860023780881600000US33618603361860Pittsfield 57 4114545 0POLYGON ((-71.34923 43.2990...
23367460026297381600000US33674603367460Sanbornville57 4106641 3626POLYGON ((-71.04123 43.5649...
33387140023780991600000US33871403387140Woodsville 57 2271155 63295POLYGON ((-72.04107 44.1542...
43312900008735671600000US33129003312900Claremont 251117661372323426POLYGON ((-72.41538 43.3802...
53311300023780551600000US33113003311300Charlestown 57 2107485 121062POLYGON ((-72.43447 43.2304...
63378340026297441600000US33783403378340Walpole 57 3152287 27471POLYGON ((-72.43535 43.0751...
 
places.nh %>%
  fortify(region = "GEOID") -> nh.df

names(nh.df)
  1. 'STATEFP'
  2. 'PLACEFP'
  3. 'PLACENS'
  4. 'AFFGEOID'
  5. 'GEOID'
  6. 'NAME'
  7. 'LSAD'
  8. 'ALAND'
  9. 'AWATER'
  10. 'geometry'
nh.df %>%
    head()
A sf: 6 × 10
STATEFPPLACEFPPLACENSAFFGEOIDGEOIDNAMELSADALANDAWATERgeometry
<chr><chr><chr><chr><chr><chr><chr><dbl><dbl><POLYGON [°]>
13361860023780881600000US33618603361860Pittsfield 57 4114545 0POLYGON ((-71.34923 43.2990...
23367460026297381600000US33674603367460Sanbornville57 4106641 3626POLYGON ((-71.04123 43.5649...
33387140023780991600000US33871403387140Woodsville 57 2271155 63295POLYGON ((-72.04107 44.1542...
43312900008735671600000US33129003312900Claremont 251117661372323426POLYGON ((-72.41538 43.3802...
53311300023780551600000US33113003311300Charlestown 57 2107485 121062POLYGON ((-72.43447 43.2304...
63378340026297441600000US33783403378340Walpole 57 3152287 27471POLYGON ((-72.43535 43.0751...
 
ggplot() +
    geom_polygon(
        data = subset(state.df, state_name == "New Hampshire"),
        aes(x = long, y = lat, group = group),
        fill = "white", color = "black"
        ) + 
    geom_sf(
        data = nh.df,
        aes(fill = GEOID)
        ) +
  ggthemes::theme_map() +
  theme(legend.position = "none")
../../../_images/base_35_01.png 
load("data/nh.data.RData")

head(nh.data)
A tibble: 6 × 3
GEOIDNAMEpopulation
<chr><chr><dbl>
3300980Alton CDP, New Hampshire 168
3301220Amherst CDP, New Hampshire 709
3301620Antrim CDP, New Hampshire 1232
3301940Ashland CDP, New Hampshire 1353
3303620Bartlett CDP, New Hampshire 104
3304660Belmont CDP, New Hampshire 1814
 
nh.df %>%
  left_join(
      nh.data, 
      by = c("GEOID" = "GEOID")
  ) -> nh

left_join: added 3 columns (NAME.x, NAME.y, population)

           > rows only in x    0

           > rows only in y  ( 0)

           > matched rows     97

           >                 ====

           > rows total       97

head(nh)
A sf: 6 × 12
STATEFPPLACEFPPLACENSAFFGEOIDGEOIDNAME.xLSADALANDAWATERNAME.ypopulationgeometry
<chr><chr><chr><chr><chr><chr><chr><dbl><dbl><chr><dbl><POLYGON [°]>
13361860023780881600000US33618603361860Pittsfield 57 4114545 0Pittsfield CDP, New Hampshire 1586POLYGON ((-71.34923 43.2990...
23367460026297381600000US33674603367460Sanbornville57 4106641 3626Sanbornville CDP, New Hampshire 581POLYGON ((-71.04123 43.5649...
33387140023780991600000US33871403387140Woodsville 57 2271155 63295Woodsville CDP, New Hampshire 903POLYGON ((-72.04107 44.1542...
43312900008735671600000US33129003312900Claremont 251117661372323426Claremont city, New Hampshire 13016POLYGON ((-72.41538 43.3802...
53311300023780551600000US33113003311300Charlestown 57 2107485 121062Charlestown CDP, New Hampshire 1029POLYGON ((-72.43447 43.2304...
63378340026297441600000US33783403378340Walpole 57 3152287 27471Walpole CDP, New Hampshire 519POLYGON ((-72.43535 43.0751...
 
ggplot() +
  geom_polygon(
    data = subset(
        state.df, 
        state_name == "New Hampshire"
    ),
    aes(
        x = long, 
        y = lat, 
        group = group
    ),
      fill = "white", 
      color = "black"
    ) +
  geom_sf(
    data = nh,
    aes(fill = population)
    ) +
  coord_sf() +
  scale_fill_viridis_c(option = "viridis") + 
  ggthemes::theme_map() +
  theme(
      legend.position = "bottom",
      legend.text = element_text(size = 14),
      legend.key.width = unit(5, 'cm'),
      title = element_text(size = 20, face = "bold")  
  ) +
  labs(
    fill = "Population Size",
    title = "Population Distribution in New Hampshire's Places",
    subtitle = "(American Community Survey, 2014-2018)"
       )
../../../_images/base_39_01.png 
readr::read_csv(
    "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-12-03/tickets.csv"
    ) %>%
  mutate(
    year = lubridate::year(issue_datetime),
    month = lubridate::month(issue_datetime)
         ) %>%
  filter(month == 12, lon > -75.5) %>%
  sample_frac(0.2) -> tickets

Rows: 1260891 Columns: 7

── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (2): violation_desc, issuing_agency
dbl  (4): fine, lat, lon, zip_code
dttm (1): issue_datetime

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

mutate: new variable 'year' (double) with one unique value and 0% NA

        new variable 'month' (double) with 12 unique values and 0% NA

filter: removed 1,169,690 rows (93%), 91,201 rows remaining

sample_frac: removed 72,961 rows (80%), 18,240 rows remaining

tickets %>%
  unite(
      display, 
      c(issuing_agency, issue_datetime, fine),
      sep = "; ", 
      remove = FALSE
  ) -> tickets

library(leaflet)
library(htmltools)
library(widgetframe)

Loading required package: htmlwidgets

leaflet(tickets) %>%
  addTiles() %>%
  addCircles(
      lng = ~ lon, 
      lat = ~ lat, 
      popup = ~htmlEscape(display),
      color = "steelblue", 
      opacity = 0.10
  ) 

readr::read_csv(
    "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv"
    ) -> covid 

Rows: 2050611 Columns: 6

── Column specification ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr  (3): county, state, fips
dbl  (2): cases, deaths
date (1): date

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

covid %>%
  filter(
      state == "Ohio", date == "2020-04-17"
  ) -> cov19

filter: removed 2,050,524 rows (>99%), 87 rows remaining

library(housingData)

geoCounty %>%
  filter(state == "OH") %>%
  separate(
      county,
      into = c("countyname", "extra"),
      sep = " County",
      remove = TRUE
  ) %>%
  mutate(
      countyname = stringr::str_to_sentence(countyname)
  ) -> oh

filter: removed 2,987 rows (97%), 88 rows remaining

mutate: changed one value (1%) of 'countyname' (0 new NA)

oh %>%
  left_join(
      cov19, 
      by = c("countyname" = "county")
  ) -> ohcov19

head(ohcov19)

left_join: added 7 columns (fips.x, state.x, date, state.y, fips.y, …)

           > rows only in x    2

           > rows only in y  ( 1)

           > matched rows     86

           >                 ====

           > rows total       88
A data.frame: 6 × 13
fips.xcountynameextrastate.xlonlatrMapStaterMapCountydatestate.yfips.ycasesdeaths
<fct><chr><chr><fct><dbl><dbl><fct><fct><date><chr><chr><dbl><dbl>
139001Adams OH-83.4635938.85662ohioadams 2020-04-17Ohio39001 30
239003Allen OH-84.1082540.77675ohioallen 2020-04-17Ohio39003659
339005Ashland OH-82.2692240.86122ohioashland 2020-04-17Ohio39005 50
439007AshtabulaOH-80.7564741.71017ohioashtabula2020-04-17Ohio39007544
539009Athens OH-82.0405339.34576ohioathens 2020-04-17Ohio39009 31
639011Auglaize OH-84.2219240.56421ohioauglaize 2020-04-17Ohio39011211
 
ohcov19 %>%
  unite(
      display, 
      c(countyname, cases), 
      sep = ": ",
      remove = FALSE
  ) -> ohcov19

head(ohcov19)
A data.frame: 6 × 14
fips.xdisplaycountynameextrastate.xlonlatrMapStaterMapCountydatestate.yfips.ycasesdeaths
<fct><chr><chr><chr><fct><dbl><dbl><fct><fct><date><chr><chr><dbl><dbl>
139001Adams: 3 Adams OH-83.4635938.85662ohioadams 2020-04-17Ohio39001 30
239003Allen: 65 Allen OH-84.1082540.77675ohioallen 2020-04-17Ohio39003659
339005Ashland: 5 Ashland OH-82.2692240.86122ohioashland 2020-04-17Ohio39005 50
439007Ashtabula: 54AshtabulaOH-80.7564741.71017ohioashtabula2020-04-17Ohio39007544
539009Athens: 3 Athens OH-82.0405339.34576ohioathens 2020-04-17Ohio39009 31
639011Auglaize: 21 Auglaize OH-84.2219240.56421ohioauglaize 2020-04-17Ohio39011211
 
leaflet(ohcov19) %>%
  addTiles() %>%
  addCircleMarkers(
    lng = ~ lon, 
    lat = ~ lat, 
    popup = ~htmlEscape(display),
    color = "salmon", 
    opacity = 0.10, 
    radius = ~sqrt(cases)
    ) 

leaflet(ohcov19) %>%
  addTiles() %>%
  addCircleMarkers(
      lng = ~ lon, 
      lat = ~ lat, 
      popup = ~htmlEscape(display),
      color = "salmon", 
      opacity = 0.10, 
      radius = ~sqrt(cases)
    ) 

library(plotly)

ggplot() +
  geom_point(
    data = mpg,
    mapping = aes(
        x = cty, 
        y = hwy, 
        color = trans)
    ) +
  labs(
      x = "City Mileage",
      y = "Highway Mileage",
      color = "Transmission"
  ) -> pl01

ggplotly(pl01)

Attaching package: ‘plotly’

The following objects are masked from ‘package:tidylog’:

    distinct, filter, group_by, mutate, rename, select, slice,
    summarise, transmute, ungroup

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

library(highcharter)

covid %>%
  filter(
      date == "2020-04-17"
  ) %>%
  rename(
      State = state, 
      `Total Cases` = cases
  ) -> tab1

Registered S3 method overwritten by 'quantmod':
  method            from
  as.zoo.data.frame zoo 

hchart(
    tab1, 
    "bar", 
    hcaes(
        x = State, 
        y = `Total Cases`
        )
    ) 

covid %>%
  filter(
      state %in% c("Ohio", "Florida", "California", "New Jersey", "Ohio", "New York"),
      date >= "2020-03-01"
  ) %>%
  group_by(state, date) %>%
  mutate(
      log_cases = log(sum(cases))
  ) %>%
    ungroup() -> tab2

head(tab2)
A tibble: 6 × 7
datecountystatefipscasesdeathslog_cases
<date><chr><chr><chr><dbl><dbl><dbl>
2020-03-01Alameda California06001103.496508
2020-03-01Humboldt California06023103.496508
2020-03-01Los AngelesCalifornia06037103.496508
2020-03-01Marin California06041103.496508
2020-03-01Napa California06055103.496508
2020-03-01Orange California06059103.496508
 
tab2 %>%
    select(state, date, log_cases) %>%
    distinct() -> tab2_nodups

head(tab2_nodups)
A tibble: 6 × 3
statedatelog_cases
<chr><date><dbl>
California2020-03-013.4965076
Florida 2020-03-010.6931472
New York 2020-03-010.0000000
California2020-03-023.6375862
Florida 2020-03-020.6931472
New York 2020-03-020.0000000
 
hchart(
    tab2_nodups, 
    "line", 
    hcaes(
        x = date, 
        y = log_cases, 
        group = state
        )
    )

covid %>%
  group_by(county, state, fips) %>%
  filter(date == "2021-11-15") %>% 
  unite(
      Location, 
      c(county, state), 
      sep = ", ", 
      remove = TRUE
  ) -> tab3

head(tab3)
A grouped_df: 6 × 5
dateLocationfipscasesdeaths
<date><chr><chr><dbl><dbl>
2021-11-15Autauga, Alabama0100110407154
2021-11-15Baldwin, Alabama0100337875581
2021-11-15Barbour, Alabama01005 3648 79
2021-11-15Bibb, Alabama 01007 4317 92
2021-11-15Blount, Alabama 0100910536188
2021-11-15Bullock, Alabama01011 1523 44
 
library(urbnmapr)

data(counties)

head(counties)
A tibble: 6 × 12
longlatorderholepiecegroupcounty_fipsstate_abbvstate_fipscounty_namefips_classstate_name
<dbl><dbl><int><lgl><fct><fct><chr><chr><chr><chr><chr><chr>
-86.9176032.664171FALSE101001.101001AL01Autauga CountyH1Alabama
-86.8165732.660122FALSE101001.101001AL01Autauga CountyH1Alabama
-86.7133932.661733FALSE101001.101001AL01Autauga CountyH1Alabama
-86.7142232.705694FALSE101001.101001AL01Autauga CountyH1Alabama
-86.4131232.707395FALSE101001.101001AL01Autauga CountyH1Alabama
-86.4111732.409946FALSE101001.101001AL01Autauga CountyH1Alabama
 
counties %>%
  separate(
      county_fips, 
      into = c("stfips", "fips"), 
      sep = 2, 
      remove = FALSE
      ) %>%
  mutate(
      leader = "us", 
      stlower = stringr::str_to_lower(state_abbv)
      ) %>%
  unite(
      code, 
      c(leader, stlower, fips), 
      sep = "-"
      ) -> cdf

head(cdf)
A tibble: 6 × 14
longlatorderholepiecegroupcounty_fipsstfipscodestate_abbvstate_fipscounty_namefips_classstate_name
<dbl><dbl><int><lgl><fct><fct><chr><chr><chr><chr><chr><chr><chr><chr>
-86.9176032.664171FALSE101001.10100101us-al-001AL01Autauga CountyH1Alabama
-86.8165732.660122FALSE101001.10100101us-al-001AL01Autauga CountyH1Alabama
-86.7133932.661733FALSE101001.10100101us-al-001AL01Autauga CountyH1Alabama
-86.7142232.705694FALSE101001.10100101us-al-001AL01Autauga CountyH1Alabama
-86.4131232.707395FALSE101001.10100101us-al-001AL01Autauga CountyH1Alabama
-86.4111732.409946FALSE101001.10100101us-al-001AL01Autauga CountyH1Alabama
 
cdf %>%
  select(code, county_fips) %>%
  distinct() -> cdf2

head(cdf2)
A tibble: 6 × 2
codecounty_fips
<chr><chr>
us-al-00101001
us-al-00301003
us-al-00501005
us-al-00701007
us-al-00901009
us-al-01101011
 
tab3 %>%
  left_join(
      cdf2, 
      by = c("fips" = "county_fips")
  ) -> tab4

left_join: added one column (code)

           > rows only in x     117

           > rows only in y  (    9)

           > matched rows     3,133

           >                 =======

           > rows total       3,250

head(tab4)
A grouped_df: 6 × 6
dateLocationfipscasesdeathscode
<date><chr><chr><dbl><dbl><chr>
2021-11-15Autauga, Alabama0100110407154us-al-001
2021-11-15Baldwin, Alabama0100337875581us-al-003
2021-11-15Barbour, Alabama01005 3648 79us-al-005
2021-11-15Bibb, Alabama 01007 4317 92us-al-007
2021-11-15Blount, Alabama 0100910536188us-al-009
2021-11-15Bullock, Alabama01011 1523 44us-al-011
 
library(viridis)

hcmap("countries/us/us-all-all", 
      data = tab4,
      name = "COVID-19 Cases", value = "cases",
      joinBy = c("hc-key", "code"),
      borderColor = "steelblue") %>%
  hc_colorAxis(stops = color_stops(10, rev(magma(10)))) %>% 
  hc_legend(layout = "horizontal", align = "right",
            floating = TRUE, valueDecimals = 0, valueSuffix = ""
           )

Loading required package: viridisLite

tab4 %>%
  filter(
      grepl("-oh-", code)
  ) -> tab5

head(tab5)
A grouped_df: 6 × 6
dateLocationfipscasesdeathscode
<date><chr><chr><dbl><dbl><chr>
2021-11-15Adams, Ohio 39001 4360105us-oh-001
2021-11-15Allen, Ohio 3900317443312us-oh-003
2021-11-15Ashland, Ohio 39005 7413146us-oh-005
2021-11-15Ashtabula, Ohio3900711166217us-oh-007
2021-11-15Athens, Ohio 39009 8083 93us-oh-009
2021-11-15Auglaize, Ohio 39011 7334110us-oh-011
 
hcmap("countries/us/us-oh-all", 
      data = tab5,
      name = "COVID-19 Cases", value = "cases",
      joinBy = c("hc-key", "code"),
      borderColor = "steelblue") %>%
  hc_colorAxis(stops = color_stops(10, rev(magma(10)))) %>% 
  hc_legend(layout = "horizontal", align = "right",
            floating = TRUE, valueDecimals = 0, valueSuffix = ""
           )

library(tidyverse)

data(USArrests)
names(USArrests)
USArrests$statename <- rownames(USArrests)

head(USArrests)
  1. 'Murder'
  2. 'Assault'
  3. 'UrbanPop'
  4. 'Rape'
A data.frame: 6 × 5
MurderAssaultUrbanPopRapestatename
<dbl><int><int><dbl><chr>
Alabama13.22365821.2Alabama
Alaska10.02634844.5Alaska
Arizona 8.12948031.0Arizona
Arkansas 8.81905019.5Arkansas
California 9.02769140.6California
Colorado 7.92047838.7Colorado